Gelişmiş otsu eşiği ile çok düzeyli görüntü eşiği için aes crypto ile birleştirilen black wıdow optimizasyon algoritması

Thresholding images, a technique that uses histogram analysis, is an important aspect of the study of image processing. These methods an image's histogram and recommend optimal threshold settings for discriminating between image regions. Since thresholding achieves high accuracy and efficiency, it is widely employed in studies involving picture segmentation. Among the most significant thresholding techniques in image processing is called multi-level thresholding, and it incorporates the Otsu approach, which is utilized by a large number of people. While these approaches are effective and efficient, they are also very computationally intensive. Increases in the number of criteria employed cause these strategies to become less effective because of their increasing complexity and execution time. The Black Widow Spider Optimization Algorithm is one example of a metaheuristic algorithm that can be utilized as part of the Otsu threshold technique to help find good threshold values. These algorithms can determine, in a reasonable time, which thresholds are most effective for a given image. The suggested technique considers each threshold as a constituent or facet of a solution to the Black Widow Spider Optimization Algorithm in order to determine the optimal threshold value with minimal intricacy. Cryptographic techniques are gaining prominence as a means of guaranteeing safe data transit across a wide range of use cases. As more and more industries begin to rely on visual processes, protecting sensitive proprietary image data becomes more vital than ever. When it comes to protecting sensitive information, nothing beats the AES, a block cipher that has numerous benefits. In many image processing applications, multilevel thresholding image segmentation is popular. Thresholding uses gray levels to separate an object from its background in photo segmentation. Metaheuristic methods like the Black Widow Optimization (BWO) Algorithm can determine the Otsu threshold. The study's goal is to prove that employing an iris template may provide a cryptographic key that is both secure and difficult to compromise. An iris template or code is generated from the processed iris pictures and utilized for security purposes. During this phase, BWO method is used for image thresholding. For data protection purposes, we segment the iris from an eye image before encrypting and decrypting it using the advanced encryption standard (AES). Experimental results reveal that iris image encryption and decryption techniques were quick and secure. The results reveal that the suggested technique has a higher PSNR index value than the competing algorithms in 83.33 percent of the experiments, and a higher SSIM index value in 80 percent of the experiments. The suggested algorithm's thresholding performance on medical photos like brain tumours and human retinal images is demonstrated by an analysis of a set of pertussis images.